IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v1y2004i3p211-230.html
   My bibliography  Save this article

Pattern classification by goal programming and support vector machines

Author

Listed:
  • Takeshi Asada
  • Yeboon Yun
  • Hirotaka Nakayama
  • Tetsuzo Tanino

Abstract

Support Vector Machines (SVMs) are now very popular as a powerful method in pattern classification problems. One of main features of SVMs is to produce a separating hyperplane which maximizes the margin in feature space induced by nonlinear mapping using kernel function. As a result, SVMs can treat not only linear separation but also nonlinear separation. While the soft margin method of SVMs considers only the distance between separating hyperplane and misclassified data, we propose in this paper multi-objective programming formulation considering surplus variables. A similar formulation was extensively researched in linear discriminant analysis mostly in 1980s by using Goal Programming(GP). This paper compares these conventional methods such as SVMs and GP with our proposed formulation through several examples. Copyright Springer-Verlag Berlin/Heidelberg 2004

Suggested Citation

  • Takeshi Asada & Yeboon Yun & Hirotaka Nakayama & Tetsuzo Tanino, 2004. "Pattern classification by goal programming and support vector machines," Computational Management Science, Springer, vol. 1(3), pages 211-230, October.
    • Handle: RePEc:spr:comgts:v:1:y:2004:i:3:p:211-230
    DOI: 10.1007/s10287-004-0013-x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10287-004-0013-x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10287-004-0013-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comgts:v:1:y:2004:i:3:p:211-230. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.